CHIDONG ZHANG: Last but not least, lightning talk of climate weather research is given by Nick Bond on The Blob. 

AUDIENCE: Yeah!

[LAUGHING] 

NICK BOND: Yeah. I'm very pleased to be on the [INAUDIBLE] right here. I like this backdrop for this slide because I'm going to looking a little bit into the subsurface properties of the recent marine heat wave [INAUDIBLE] blast. 

OK. Yeah. The Blob, right? Unprecedented in intensity, in magnitude, geographic scope, duration and so forth. And one thing I'd like to point out is that we were at kind of leading edge of this in terms of alerting the climate community to it. I am a member of the EcoFOCI group, but different groups work together here. And I want to give props to Meghan Cronin for really pushing to get a paper out while this event was in progress. And yeah, [INAUDIBLE] 

Of course, it had had some major impacts, massive harmful algal bloom, a huge die-off of some seabird populations and so forth. We'd like to be able to anticipate these sort of events. And that's another thing that we're at the forefront around here, and that is seasonal marine ecosystem prediction. But we have a lot of horsepower here in Seattle with the two fishery labs-- UW/JISAO and PMEL. 

The whole idea here is that we use the output from climate models used for seasonal weather prediction, dry ocean regional ocean models dynamical downscaling. Usually those have direct simulation of lower trophic level properties in heat chemistry and so forth, sometimes empirical techniques for getting upper trophic levels. And this works best when the scientists can regularly interact with the managers. 

The J-SCOPE every year for the Pacific Northwest coast, I would say, is a pioneering effort. And one thing that I think is especially significant there-- pardon me, before I forget, the maps on the right is that we have found that subsurface forecasts of bottom temperature and bottom oxygen concentration is actually better and more skilled than the sea surface temperature forecasts, as illustrated by those of correlation maps read and being better than otherwise. So that helped motivate Phyllis Stabeno and I to look into this Gulf of Alaska event. 

I just want to get started with the sea surface temperature anomaly maps. If you look at the two maps on the right there, the summer of 2019 at the bottom and the summer of 2014 at the top, they look quite similar. But every event is different, of course. And note that the previous winters, the Blob, previous Blob, got its start in the winter of 2013-2014, while the 2019 event really revved up in the summer. 

Note also that little box off Kodiak Island in the lower right hand plot. That is near where there's a commercial fishery for Pacific cod. The cod, adult cod anyway, like other ground fish, don't hang out to the surface. And so the conditions that matter to them are at depth. 

The time series at the left refers to over the 100 to 250 meter layer. What those temperatures are, going back to 1980 from the GODAS product. Note that it was actually quite a bit warmer in 2019 than in 2015 in that area at that depth. Another perspective is supported by those profiles shown at the right. And again, this is from GODAS. We're comparing it with other reanalysis products and with the gridded data from Argo. 

So how did it get so warm? Here's a series of maps at the top for the summer of 2018, winter of 2018-2019, and then last summer. Again, the 100 to 250 meter layer, and showing temperature anomalies. There were some pre-existing warmth at depth of-- hung over from the Blob. 

And then last winter, that is the winter of 2018-2019, it actually warmed up, according to GODAS, because of warmer water near the coast being affected anomalously offshore and downward. And then that warm water was carried around the Gulf of Alaska in the mean circulations of it to the Western side. 

This work is all in progress. But I just use it to illustrate the sort of things we want to do about figuring out the dynamics of these events, their potential predictability, and finally, what their probable impact scale level could be. 

So thank you.

[APPLAUSE]